Blog•Apr 18, 2026
My Workflow for Understanding LLM Architectures
The author outlines a hands‑on workflow for decoding large language model (LLM) architectures, starting with official papers but quickly shifting to Hugging Face model‑hub config files and the Transformers codebase when papers lack detail. By inspecting the configuration and runnable reference implementation, practitioners can extract layer types, dimensions, and other architectural nuances. The process is deliberately manual, emphasizing learning through direct code analysis, and applies only to open‑weight models, not proprietary systems like ChatGPT or Gemini.
